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Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics

Author

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  • Jianjun Luo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ziming Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Liang Xu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Aurelia Chi Wang

    (Georgia Institute of Technology)

  • Kai Han

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Tao Jiang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Qingsong Lai

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yu Bai

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Wei Tang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Feng Ru Fan

    (Purdue University, West Lafayette)

  • Zhong Lin Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Georgia Institute of Technology)

Abstract

In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self-powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.

Suggested Citation

  • Jianjun Luo & Ziming Wang & Liang Xu & Aurelia Chi Wang & Kai Han & Tao Jiang & Qingsong Lai & Yu Bai & Wei Tang & Feng Ru Fan & Zhong Lin Wang, 2019. "Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13166-6
    DOI: 10.1038/s41467-019-13166-6
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    Cited by:

    1. Ze-Qi Lu & Long Zhao & Hai-Ling Fu & Eric Yeatman & Hu Ding & Li-Qun Chen, 2024. "Ocean wave energy harvesting with high energy density and self-powered monitoring system," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Beibei Shao & Ming-Han Lu & Tai-Chen Wu & Wei-Chen Peng & Tien-Yu Ko & Yung-Chi Hsiao & Jiann-Yeu Chen & Baoquan Sun & Ruiyuan Liu & Ying-Chih Lai, 2024. "Large-area, untethered, metamorphic, and omnidirectionally stretchable multiplexing self-powered triboelectric skins," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Qiuhong Yu & Rui Ge & Juan Wen & Tao Du & Junyi Zhai & Shuhai Liu & Longfei Wang & Yong Qin, 2022. "Highly sensitive strain sensors based on piezotronic tunneling junction," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Zhao, Huai & Ouyang, Huajiang, 2021. "A capsule-structured triboelectric energy harvester with stick-slip vibration and vibro-impact," Energy, Elsevier, vol. 235(C).
    5. Patnam, Harishkumarreddy & Dudem, Bhaskar & Graham, Sontyana Adonijah & Yu, Jae Su, 2021. "High-performance and robust triboelectric nanogenerators based on optimal microstructured poly(vinyl alcohol) and poly(vinylidene fluoride) polymers for self-powered electronic applications," Energy, Elsevier, vol. 223(C).
    6. Wenbo Liu & Youning Duo & Jiaqi Liu & Feiyang Yuan & Lei Li & Luchen Li & Gang Wang & Bohan Chen & Siqi Wang & Hui Yang & Yuchen Liu & Yanru Mo & Yun Wang & Bin Fang & Fuchun Sun & Xilun Ding & Chi Zh, 2022. "Touchless interactive teaching of soft robots through flexible bimodal sensory interfaces," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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